Title: KRAS, BRAF, and TP53 Deep Sequencing for Colorectal Carcinoma Patient Diagnostics
Abstract: In colorectal carcinoma, KRAS (alias Ki-ras) and BRAF mutations have emerged as predictors of resistance to anti–epidermal growth factor receptor antibody treatment and worse patient outcome, respectively. In this study, we aimed to establish a high-throughput deep sequencing workflow according to 454 pyrosequencing technology to cope with the increasing demand for sequence information at medical institutions. A cohort of 81 patients with known KRAS mutation status detected by Sanger sequencing was chosen for deep sequencing. The workflow allowed us to analyze seven amplicons (one BRAF, two KRAS, and four TP53 exons) of nine patients in parallel in one deep sequencing run. Target amplification and variant calling showed reproducible results with input DNA derived from FFPE tissue that ranged from 0.4 to 50 ng with the use of different targets and multiplex identifiers. Equimolar pooling of each amplicon in a deep sequencing run was necessary to counterbalance differences in patient tissue quality. Five BRAF and 49 TP53 mutations with functional consequences were detected. The lowest mutation frequency detected in a patient tumor population was 5% in TP53 exon 5. This low-frequency mutation was successfully verified in a second PCR and deep sequencing run. In summary, our workflow allows us to process 315 targets a week and provides the quality, flexibility, and speed needed to be integrated as standard procedure for mutational analysis in diagnostics. In colorectal carcinoma, KRAS (alias Ki-ras) and BRAF mutations have emerged as predictors of resistance to anti–epidermal growth factor receptor antibody treatment and worse patient outcome, respectively. In this study, we aimed to establish a high-throughput deep sequencing workflow according to 454 pyrosequencing technology to cope with the increasing demand for sequence information at medical institutions. A cohort of 81 patients with known KRAS mutation status detected by Sanger sequencing was chosen for deep sequencing. The workflow allowed us to analyze seven amplicons (one BRAF, two KRAS, and four TP53 exons) of nine patients in parallel in one deep sequencing run. Target amplification and variant calling showed reproducible results with input DNA derived from FFPE tissue that ranged from 0.4 to 50 ng with the use of different targets and multiplex identifiers. Equimolar pooling of each amplicon in a deep sequencing run was necessary to counterbalance differences in patient tissue quality. Five BRAF and 49 TP53 mutations with functional consequences were detected. The lowest mutation frequency detected in a patient tumor population was 5% in TP53 exon 5. This low-frequency mutation was successfully verified in a second PCR and deep sequencing run. In summary, our workflow allows us to process 315 targets a week and provides the quality, flexibility, and speed needed to be integrated as standard procedure for mutational analysis in diagnostics. Over the past decade, second-generation sequencing technology has revolutionized basic and medical research and is entering daily routine diagnostics. The reproducibility and consistency of the sequencing data are now comparable with Sanger sequencing. However, the costs of whole genome or exome sequencing are still high. Several platforms to address this problem have been released in the past 2 years, combining targeted re-sequencing of specific cancer genes with lower throughput and costs. A detailed analysis between three of these platforms1Loman N.J. Misra R.V. Dallman T.J. Constantinidou C. Gharbia S.E. Wain J. Pallen M.J. 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Martini M. Bardelli A. Siena S. Sartore-Bianchi A. Tabernero J. Macarulla T. Di Fiore F. Gangloff A.O. Ciardiello F. Pfeiffer P. Qvortrup C. Hansen T.P. Van Cutsem E. Piessevaux H. Lambrechts D. Delorenzi M. Tejpar S. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis.Lancet Oncol. 2010; 11: 753-762Abstract Full Text Full Text PDF PubMed Scopus (1710) Google Scholar, 13Van Cutsem E. Kohne C.H. Lang I. Folprecht G. Nowacki M.P. Cascinu S. Shchepotin I. Maurel J. Cunningham D. Tejpar S. Schlichting M. Zubel A. Celik I. Rougier P. Ciardiello F. Cetuximab plus irinotecan, fluorouracil, and leucovorin as first-line treatment for metastatic colorectal cancer: updated analysis of overall survival according to tumor KRAS and BRAF mutation status.J Clin Oncol. 2011; 29: 2011-2019Crossref PubMed Scopus (1506) Google Scholar, 14Popovici V. Budinska E. 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Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR.Nature. 2012; 483: 100-103Crossref PubMed Scopus (1493) Google Scholar However, the authors identified a feedback activation of EGFR and suggested that these patients might benefit from a combination therapy, including inhibition of EGFR and BRAF. Secondary mutations in KRAS after anti-EGFR therapy which confer resistance to treatment were also found in colorectal carcinoma.17Misale S. Yaeger R. Hobor S. Scala E. Janakiraman M. Liska D. Valtorta E. Schiavo R. Buscarino M. Siravegna G. Bencardino K. Cercek A. Chen C.T. Veronese S. Zanon C. Sartore-Bianchi A. Gambacorta M. Gallicchio M. Vakiani E. Boscaro V. Medico E. Weiser M. Siena S. Di Nicolantonio F. Solit D. Bardelli A. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer.Nature. 2012; 486: 432-536Google Scholar In general, response leading to resistance to chemotherapy or targeted therapy is a common phenomenon that results in better progression-free survival, but mostly not in overall survival. The tumor suppressor p53 is well known to modulate response to chemotherapy and was proposed to play a direct role in cetuximab response in patients with metastatic colorectal carcinoma.18Oden-Gangloff A. Di Fiore F. Bibeau F. Lamy A. Bougeard G. Charbonnier F. Blanchard F. Tougeron D. Ychou M. Boissiere F. Le Pessot F. Sabourin J.C. Tuech J.J. Michel P. Frebourg T. TP53 mutations predict disease control in metastatic colorectal cancer treated with cetuximab-based chemotherapy.Br J Cancer. 2009; 100: 1330-1335Crossref PubMed Scopus (80) Google Scholar Although up to 50% of all patients with colorectal carcinoma exhibit TP53 mutations, nontargeted chemotherapy is often used as first-line treatment without knowledge of its mutation status.13Van Cutsem E. Kohne C.H. Lang I. Folprecht G. Nowacki M.P. Cascinu S. Shchepotin I. Maurel J. Cunningham D. Tejpar S. Schlichting M. Zubel A. Celik I. Rougier P. Ciardiello F. Cetuximab plus irinotecan, fluorouracil, and leucovorin as first-line treatment for metastatic colorectal cancer: updated analysis of overall survival according to tumor KRAS and BRAF mutation status.J Clin Oncol. 2011; 29: 2011-2019Crossref PubMed Scopus (1506) Google Scholar How mutations in TP53 translate into altered protein expression and render it nonfunctional is a controversial topic in literature. However, in a recent publication, we showed that TP53 is an independent and strong predictor of survival.19Wild P.J. Ikenberg K. Fuchs T.J. Rechsteiner M. Georgiev S. Fankhauser N. Noske A. Roessle M. Caduff R. Dellas A. Fink D. Moch H. Krek W. Frew I.J. p53 suppresses type II endometrial carcinomas in mice and governs endometrial tumour aggressiveness in humans.EMBO Mol Med. 2012; 4: 808-824Crossref Scopus (49) Google Scholar In this study, we aimed to establish a high-throughput deep sequencing platform on the basis of 454 pyrosequencing technology to cope with the increasing demand for sequence information at medical institutions. With the use of this platform, we intended to unravel low-frequency mutations below the detection limit of Sanger sequencing and to show throughput power for future diagnostics. As a model to implement our platform, we used a cohort of patients with diagnosed colorectal carcinoma. As first-line targets, we chose KRAS exon 2 and BRAF exon 15, because they were suggested to give most information about treatment outcome.10De Roock W. Claes B. Bernasconi D. De Schutter J. Biesmans B. Fountzilas G. Kalogeras K.T. Kotoula V. Papamichael D. Laurent-Puig P. Penault-Llorca F. Rougier P. Vincenzi B. Santini D. Tonini G. Cappuzzo F. Frattini M. Molinari F. Saletti P. De Dosso S. Martini M. Bardelli A. Siena S. Sartore-Bianchi A. Tabernero J. Macarulla T. Di Fiore F. Gangloff A.O. Ciardiello F. Pfeiffer P. Qvortrup C. Hansen T.P. Van Cutsem E. Piessevaux H. Lambrechts D. Delorenzi M. Tejpar S. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis.Lancet Oncol. 2010; 11: 753-762Abstract Full Text Full Text PDF PubMed Scopus (1710) Google Scholar Furthermore, we included KRAS exon 3 to investigate more deeply the effect of mutations in codon 61 to treatment response. Finally, we added TP53 to the analyzing panel because of its well-known role in chemotherapy resistance and to gain insight into its function in concert with altered KRAS or BRAF. In this study, we included colorectal carcinomas of 158 patients who were diagnosed at the Institute of Surgical Pathology, University Hospital Zurich (Switzerland) between 2006 and 2011. One hundred fifty-eight patients were analyzed for mutations in KRAS as indicator for anti-EGFR therapy response to cetuximab or panitumumab by Sanger sequencing in our routine diagnostic laboratory. To detect potential low-frequency mutations in KRAS that were missed by Sanger sequencing, we selected 79 patients without KRAS mutations detected by Sanger sequencing. We added two patients to this cohort with known KRAS mutation as a control. Clinicopathologic data of all patients are listed in Table 1. The median age of these patients at the time of surgery or biopsy was 61 years, ranging from 31 to 83 years. Data on chemotherapy were known for 18 patients and combination chemotherapy together with anti-EGFR therapy for 17 patients.Table 1Clinicopathologic Parameters and Mutation Status of 81 Patients with Colorectal CarcinomaCr no.Age (years)SexGradeTNMKRASBRAFTP53Deep SeqSangerDeep SeqSangerDeep Seqcr160M3401WTWTWTWTY234C (71%)∗TP53 mutations confirmed by Sanger sequencing.cr2NKF230NKWTWTWTWTWTcr3NKM331NKWTWTWTWTdel37 (52%)∗TP53 mutations confirmed by Sanger sequencing., Y234C (5%)cr467FNKNKNKNKWTWTWTWTEs.s.s. (64%)∗TP53 mutations confirmed by Sanger sequencing.cr576M232NKWTWTWTWTWTcr658MNKNKNK1WTWTWTWTWTcr765FFNKNK1WTWTWTWTWTcr846MNK301WTWTV600E (39%)V600ER175H (71%)∗TP53 mutations confirmed by Sanger sequencing.cr958F331NKG13D (57%)G13DWTWTR280G (74%)∗TP53 mutations confirmed by Sanger sequencing.cr10NKM241NKWTWTWTWTR175H (85%)∗TP53 mutations confirmed by Sanger sequencing.cr11NKM342NKWTWTWTWTWTcr1238F342NKWTWTWTWTWTcr1358M2300WTWTWTWTWTcr1454F2220WTWTWTWTWTcr15NKF23NK1WTWTWTWTEs.s.s. (21%), intronic (27%)∗TP53 mutations confirmed by Sanger sequencing.cr1631MNK310WTWTWTWTWTcr1773FNK311WTWTWTWTN239S (80%)∗TP53 mutations confirmed by Sanger sequencing.cr1863M2301WTWTWTWTdel13 (88%)∗TP53 mutations confirmed by Sanger sequencing.cr1961MNKNKNKNKWTWTWTWTWTcr2041M3411WTWTWTWTI195F (91%)∗TP53 mutations confirmed by Sanger sequencing.cr2155M2301WTWTWTWTQ144†STOP codon. (61%)∗TP53 mutations confirmed by Sanger sequencing.cr2278FNK4NK1WTWTWTWTWTcr2343F2321WTWTWTWTR196 (91%)∗TP53 mutations confirmed by Sanger sequencing.cr24NKMNKNKNKNKWTWTWTWTdel37 (26%)∗TP53 mutations confirmed by Sanger sequencing.cr26NKMNKNKNKNKWTWTWTWTWTcr2776M3311WTWTWTWTdel2 (48%)∗TP53 mutations confirmed by Sanger sequencing.cr2846MNK311K88K (47%)WTWTWTR175H (96%), R213R (13%)∗TP53 mutations confirmed by Sanger sequencing.cr2942MNKNKNKNKWTWTWTWTF134L (88%)¶cr30NKMNKNKNKNKWTWTWTWTR181P (37%)∗TP53 mutations confirmed by Sanger sequencing., R248W (26%)cr3148F2411WTWTWTWTIntronic (40%)∗TP53 mutations confirmed by Sanger sequencing.cr3267M3200WTWTWTWTWTcr33NKMNKNKNKNKWTWTWTWTP190L (86%)∗TP53 mutations confirmed by Sanger sequencing.cr3464M242NKWTWTWTWTR248W (60%)¶cr3561MNK31NKWTWTWTWTC176F (11%)∗TP53 mutations confirmed by Sanger sequencing., R248W (26%)cr3656M342NKWTWTWTWTR306†STOP codon. (67%)∗TP53 mutations confirmed by Sanger sequencing.cr37NKMNKNKNKNKWTWTWTWTR175H (44%)∗TP53 mutations confirmed by Sanger sequencing.cr38NKMNKNKNKNKWTWTWTWTP151T (44%)∗TP53 mutations confirmed by Sanger sequencing.cr3935M3320WTWTWTWTG245S (63%)∗TP53 mutations confirmed by Sanger sequencing.cr4077M2300WTWTWTWTG245S (62%)∗TP53 mutations confirmed by Sanger sequencing.cr4170M220NKWTWTWTWTC176Y (57%)∗TP53 mutations confirmed by Sanger sequencing.cr4261M2210WTWTWTWTWTcr43NKFNKNKNKNKWTWTWTWTWTcr4446F2301WTWTWTWTP152R (38%), E286K (39%)∗TP53 mutations confirmed by Sanger sequencing.cr45NKMNKNKNKNKWTWTWTWTWTcr4673M2300WTWTWTWTR196†STOP codon. (79%)∗TP53 mutations confirmed by Sanger sequencing.cr4763MNK211WTWTWTWTR158G (46%)∗TP53 mutations confirmed by Sanger sequencing.cr4871MNK311WTWTWTWTR248Q (43%)∗TP53 mutations confirmed by Sanger sequencing.cr4968M3421WTWTWTWTWTcr50NKMNKNKNKNKWTWTWTWTWTcr5148MNKNKNKNKWTWTWTWTWTcr5359MNK110WTWTWTWTR273H (73%)∗TP53 mutations confirmed by Sanger sequencing.cr56NKFNKNKNKNKWTWTWTWTC277F (56%)∗TP53 mutations confirmed by Sanger sequencing.cr57NKMNKNKNKNKWTWTWTWTC242Y (42%)∗TP53 mutations confirmed by Sanger sequencing.cr58NKM220NKG12C (31%)G12CWTWTWTcr5952MNK400WTWTWTWTR248Q (27%)∗TP53 mutations confirmed by Sanger sequencing.cr6083M342NKWTWTV600E (27%)V600EWTcr6156MNK420WTWTWTWTWTcr6253M3320WTWTWTWTWTcr6574F232NKWTWTWTWTF270L (73%)∗TP53 mutations confirmed by Sanger sequencing.cr66NKFNKNKNKNKWTWTWTWTR213†STOP codon. (70%)∗TP53 mutations confirmed by Sanger sequencing.cr6769F2320WTWTWTWTWTcr68NKMNKNKNKNKWTWTWTWTIntronic (23%)∗TP53 mutations confirmed by Sanger sequencing.cr6960F332NKWTWTWTWTWTcr7051M3400WTWTWTWTR175H (5%)†STOP codon.cr7182M330NKWTWTWTWTR175H (36%)∗TP53 mutations confirmed by Sanger sequencing., intronic (95%)cr7276M242NKWTWTWTWTWTcr74NKF241NKWTWTWTWTWTcr75NKMNKNKNKNKWTWTWTWTG245S (40%)∗TP53 mutations confirmed by Sanger sequencing.cr76NKFNKNKNKNKWTWTWTWTV173 mol/L (40%)∗TP53 mutations confirmed by Sanger sequencing., R213R (78%)cr77NKMNKNKNKNKWTWTWTWTR248W (49%)∗TP53 mutations confirmed by Sanger sequencing., intronic (68%)cr78NKFNKNKNKNKWTWTWTWTG245D (56%)∗TP53 mutations confirmed by Sanger sequencing.cr79NKFNKNKNKNKWTWTWTWTR196∗TP53 mutations confirmed by Sanger sequencing. (72%)∗TP53 mutations confirmed by Sanger sequencing.cr80NKMNKNKNKNKWTWTWTWTR158C (40%), R282Q (53%)∗TP53 mutations confirmed by Sanger sequencing.cr81NKMNKNKNKNKWTWTK601E (63%)K601EWTcr82NKMNKNKNKNKWTWTWTWTR175H (46%)∗TP53 mutations confirmed by Sanger sequencing.cr83NKMNKNKNKNKWTWTWTWTR248Q (74%)∗TP53 mutations confirmed by Sanger sequencing.cr8465M231NKWTWTWTWTWTcr85NKF2421WTWTV600E (44%)V600EEs.s.s. (72%)∗TP53 mutations confirmed by Sanger sequencing.cr86NKM231NKWTWTWTWTWTcr8773M2421WTWTWTWTWTcr8879M2310WTWTV600E (33%)V600EWTTNM staging was determined based on extent of the primary tumor (T), spread to nearby (N) regional lymph nodes, and presence of distant metastasis (M).‡Confirmed by second PCR and deep sequencing run.F, female; M, male; Es.s.s, essential splice site; NK, not known; WT, wild-type.∗ TP53 mutations confirmed by Sanger sequencing.† STOP codon. Open table in a new tab TNM staging was determined based on extent of the primary tumor (T), spread to nearby (N) regional lymph nodes, and presence of distant metastasis (M). ‡Confirmed by second PCR and deep sequencing run. F, female; M, male; Es.s.s, essential splice site; NK, not known; WT, wild-type. Tissue samples were fixed in 4% neutral-buffered formaldehyde and embedded in paraffin. Routine H&E sections were performed for histopathologic evaluation. Tumor grade and histologic subtype were defined according to the World Health Organization classification 2003/2010. The study was approved by the Cantonal Ethics Committee of Zurich (KEK-ZH-NR: 2010-0093/0). Three 0.6-mm diameter tumor tissue cylinders were punched from each formalin-fixed, paraffin-embedded (FFPE) tissue block. Genomic DNA was extracted with DNeasy Blood & Tissue Kit 250 (Qiagen, Hilden, Germany). Quantification of DNA was done with NanoDrop. Between 0.4 and 300 ng of DNA was used as input for PCR amplification (AmpliTaq Gold; Roche, Basel, Switzerland) with fusion primers, including multiplex identifiers (MIDs) (Table 2). We applied PCR primer pairs for the amplification of target regions with known high mutational frequency (KRAS, exons 2 and 3; BRAF, exon 15; TP53, exons 5–8). The target size, amplicon size, and the percentage of guanine-cytosine (GC%) composition of the amplicons are summarized in Supplemental Table S1. Amplicons were verified on a 1% agarose gel for proper amplification. Amplicon processing was done as described by the Amplicon Library Preparation and emulsion PCR (emPCR; Lib-A) Method GS Junior Titanium Series manual from Roche. Briefly, amplicons were purified with Agencourt AMPure XP beads (Beckman Coulter, SA, Nyon, Switzerland) and quantified with the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen/Life Technologies, Lucerne, Switzerland). Sixty-three amplicons (nine patients with seven amplicons each) were mixed in equimolar ratio for one sequencing run according to the following calculation: 70,000 reads in average per deep sequencing run resulted in 1111-fold coverage of each amplicon. This allowed a detection limit of 4.5% mutation frequency with the use of our selected threshold of 50 reads. emPCR was performed with 1 × 107 molecules, and 5,00,000 enriched beads were loaded on a 454 Junior Sequencer (Roche). Demultiplexing and variant calling were done with the Amplicon Variant Analyzer (AVA) software version 3.5 from Roche. Variants with zero reads in either forward or reverse direction were discarded. In addition, only variants with at least 50 reads were included in the analysis (see Results). Variants were further analyzed with the Variant Effect Predictor from ENSEMBL (http://www.ensembl.org). To weigh the functional effect of the mutations only variants that fulfilled at least two of the following criteria were included: Mutation Assessor (http://mutationassessor.org): high, medium; PolyPhen (http://genetics.bwh.harvard.edu/pph): probably_damaging, possibly_damaging; SIFT (http://sift.jcvi.org): deleterious; VEP (http://www.ensembl.org): nonsynonymous coding missense mutations, STOP gained, STOP lost, complex indel, frame-shift coding; CHASM (http://www.chasmsoftware.org): P value < 0.05, all websites last accessed January 26, 2012.20Forman J.R. Worth C.L. Bickerton G.R. Eisen T.G. Blundell T.L. Structural bioinformatics mutation analysis reveals genotype-phenotype correlations in von Hippel-Lindau disease and suggests molecular mechanisms of tumorigenesis.Proteins. 2009; 77: 84-96Crossref PubMed Scopus (46) Google Scholar, 21Carter H. Chen S. Isik L. Tyekucheva S. Velculescu V.E. Kinzler K.W. Vogelstein B. Karchin R. Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.Cancer Res. 2009; 69: 6660-6667Crossref PubMed Scopus (342) Google ScholarTable 2Primers Used for Sanger Sequencing and Deep SequencingSanger sequencingPrimers∗Bold letters indicate deep sequencing adapters with MIDs. KRASExon 2 F5′-AAGGCCTGCTGAAAATGACTG-3′Exon 2 R5′-GGTCCTGCACCAGTAATATGCA-3′Exon 3 F5′-CCAGACTGTGTTTCTCCCTTC-3′Exon 3 R5′-TGCATGGCATTAGCAAAGAC-3′ BRAFExon 15 F5′-TCATAATGCTTGCTCTGATAGGA-3′Exon 15 R5′-GGCCAAAAATTTAATCAGTGGA-3′ TP53Exon 5 F5′-CACTTGTGCCCTGACTTTCA-3′Exon 5 R5′-AACCAGCCCTGTCGTCTCT-3′Exon 6 F5′-CAGGCCTCTGATTCCTCACT-3′Exon 6 R5′-CTTAACCCCTCCTCCCAGAG-3′Exon 7 F5′-CCACAGGTCTCCCCAAGG-3′Exon 7 R5′-CAGCAGGCCAGTGTGCAG-3′Exon 8 F5′-GCCTCTTGCTTCTCTTTTCC-3′Exon 8 R5′-TAACTGCACCCTTGGTCTCC-3′Deep sequencingPrimers∗Bold letters indicate deep sequencing adapters with MIDs. KRAS exon 2F_MID15′-CGTATCGCCTCCCTCGCGCCATCAGACGAGTGCGTAAGGCCTGCTGAAAATGACTG-3′R_MID15′-CTATGCGCCTTGCCAGCCCGCTCAGACGAGTGCGTGGTCCTGCACCAGTAATATGCA-3′F_MID25′-CGTATCGCCTCCCTCGCGCCATCAGACGCTCGACAAAGGCCTGCTGAAAATGACTG-3′R_MID25′-CTATGCGCCTTGCCAGCCCGCTCAGACGCTCGACAGGTCCTGCACCAGTAATATGCA-3′F_MID35′-CGTATCGCCTCCCTCGCGCCATCAGAGACGCACTCAAGGCCTGCTGAAAATGACTG-3′R_MID35′-CTATGCGCCTTGCCAGCCCGCTCAGAGACGCACTCGGTCCTGCACCAGTAATATGCA-3′ KRAS exon 3F_MID15′-CGTATCGCCTCCCTCGCGCCATCAGACGAGTGCGTCCAGACTGTGTTTCTCCCTTC-3′R_MID15′-CTATGCGCCTTGCCAGCCCGCTCAGACGAGTGCGTTGCATGGCATTAGCAAAGAC-3′F_MID25′-CGTATCGCCTCCCTCGCGCCATCAGACGCTCGACACCAGACTGTGTTTCTCCCTTC-3′R_MID25′-CTATGCGCCTTGCCAGCCCGCTCAGACGCTCGACATGCATGGCATTAGCAAAGAC-3′F_MID35′-CGTATCGCCTCCCTCGCGCCATCAGAGACGCACTCCCAGACTGTGTTTCTCCCTTC-3′R_MID35′-CTATGCGCCTTGCCAGCCCGCTCAGAGACGCACTCTGCATGGCATTAGCAAAGAC-3′ BRAF exon 15F_MID15′-CGTATCGCCTCCCTCGCGCCATCAGACGAGTGCGTTCATAATGCTTGCTCTGATAGGA-3′R_MID15′-CTATGCGCCTTGCCAGCCCGCTCAGACGAGTGCGTGGCCAAAAATTTAATCAGTGGA-3′F_MID25′-CGTATCGCCTCCCTCGCGCCATCAGACGCTCGACATCATAATGCTTGCTCTGATAGGA-3′R_MID25′-CTATGCGCCTTGCCAGCCCGCTCAGACGCTCGACAGGCCAAAAATTTAATCAGTGGA-3′F_MID35′-CGTATCGCCTCCCTCGCGCCATCAGAGACGCACTCTCATAATGCTTGCTCTGATAGGA-3′R_MID35′-CTATGCGCCTTGCCAGCCCGCTCAGAGACGCACTCGGCCAAAAATTTAATCAGTGGA-3′F_MID45′-CGTATCGCCTCCCTCGCGCCATCAGAGCACTGTAGTCATAATGCTTGCTCTGATAGGA-3′R_MID45′-CTATGCGCCTTGCCAGCCCGCTCAGAGCACTGTAGGGCCAAAAATTTAATCAGTGGA-3′ TP53 exon 5F_MID15′-CGTATCGCCTCCCTCGCGCCATCAGACGAGTGCGTCACTTGTGCCCTGACTTTCA-3′R_MID15′-CTATGCGCCTTGCCAGCCCGCTCAGACGAGTGCGTAACCAGCCCTGTCGTCTCT-3′F_MID25′-CGTATCGCCTCCCTCGCGCCATCAGACGCTCGACACACTTGTGCCCTGACTTTCA-3′R_MID25′-CTATGCGCCTTGCCAGCCCGCTCAGACGCTCGACAAACCAGCCCTGTCGTCTCTa-3′F_MID35′-CGTATCGCCTCCCTCGCGCCATCAGAGACGCACTCCACTTGTGCCCTGACTTTCA-3′R_MID35′-CTATGCGCCTTGCCAGCCCGCTCAGAGACGCACTCAACCAGCCCTGTCGTCTCTc-3′ TP53 exon 6F_MID15′-CGTATCGCCTCCCTCGCGCCATCAGACGAGTGCGTCAGGCCTCTGATTCCTCACT-3′R_MID15′-CTATGCGCCTTGCCAGCCCGCTCAGACGAGTGCGTCTTAACCCCTCCTCCCAGAG-3′F_MID25′-CGTATCGCCTCCCTCGCGCCATCAGACGCTCGACACAGGCCTCTGATTCCTCACT-3′R_MID25′-CTATGCGCCTTGCCAGCCCGCTCAGACGCTCGACACTTAACCCCTCCTCCCAGAG-3′F_MID35′-CGTATCGCCTCCCTCGCGCCATCAGAGACGCACTCCAGGCCTCTGATTCCTCACT-3′R_MID35′-CTATGCGCCTTGCCAGCCCGCTCAGAGACGCACTCCTTAACCCCTCCTCCCAGAG-3′ TP53 exon 7F_MID15′-CGTATCGCCTCCCTCGCGCCATCAGACGAGTGCGTCCACAGGTCTCCCCAAGG-3′R_MID15′-CTATGCGCCTTGCCAGCCCGCTCAGACGAGTGCGTCAGCAGGCCAGTGTGCAG-3′F_MID25′-CGTATCGCCTCCCTCGCGCCATCAGACGCTCGACACCACAGGTCTCCCCAAGG-3′R_MID25′-CTATGCGCCTTGCCAGCCCGCTCAGACGCTCGACACAGCAGGCCAGTGTGCAG-3′F_MID35′-CGTATCGCCTCCCTCGCG